community_leading_eigenvector: Leading Eigenvector Community Detection

In cograph: Analysis and Visualization of Complex Networks

Leading Eigenvector Community Detection

Description

Detects communities using the leading eigenvector of the modularity matrix. Hierarchical divisive algorithm.

Usage

community_leading_eigenvector(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

com_le(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

Arguments

x	Network input
weights	Edge weights. NULL uses network weights, NA for unweighted.
steps	Maximum number of splits. Default -1 (until modularity decreases).
start	Starting community structure (membership vector).
options	ARPACK options list. Default uses igraph::arpack_defaults().
callback	Optional callback function called after each split.
extra	Extra argument passed to callback.
env	Environment for callback evaluation.
...	Additional arguments passed to to_igraph

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Newman, M.E.J. (2006). Finding community structure using the eigenvectors of matrices. Physical Review E, 74, 036104.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_leading_eigenvector(g)
igraph::membership(comm)


net <- as_cograph(matrix(runif(25), 5, 5))
com_le(net)

cograph documentation built on April 1, 2026, 1:07 a.m.